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  • 统计与管理学院2017年学术报告第27

     

    【主  题】Full Likelihood Inference for Abundance from Continuous-Time Capture-Recapture Data

    【报告人】刘玉坤, 副教授

    华东师范大学

    【时  间】 2017年05月16日(星期二)15:00-16:00

    【地  点】 上海财经大学统计与管理学院大楼1208室

    【摘  要】Capture-recapture experiments are widely-used cost-effective sampling techniques for estimating population sizes or abundances in biology, ecology, demography, epidemiology, and reliability studies. For continuous-time capture-recapture data, existing estimation methods are based on conditional likelihoods and an inverse weighting estimating equation. The corresponding Wald-type confidence intervals for the abundance may have severe under-coverages from nominal levels, and their lower limits can be even less than the number of individuals ever captured. We propose a full likelihood inference approach by combining a parametric or partial likelihood with empirical likelihood. Under both parametric and semi-parametric intensity models, we demonstrate that the maximum likelihood estimator attains the semi-parametric efficiency lower bound and that the full likelihood ratio statistic for the abundance is asymptotically chi-square with one degree of freedom. Simulations indicate that compared with conditional-likelihood-based methods, the maximum full likelihood estimator has smaller mean square error and the likelihood ratio confidence intervals often have remarkable gains in coverage probability. We illustrate the proposed approach through analyzing a capture-recapture data-set on the bird species Prinia flaviventris in Hong Kong.

    【邀请人】 柏杨、黄涛